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Seismic Charge Sonic Mine Sound Effect [2021]

Geologists often use seismic surveys on land and in the ocean to find the right places to drill natural gas and oil wells. Seismic surveys create and measure seismic waves in the earth to get information on the geology of rock formations. Seismic surveys on land may use a thumper truck, which has a vibrating pad that pounds the ground to create seismic waves in the underlying rock. Sometimes, small amounts of explosives are used. Seismic surveys conducted in the ocean use blasts of sound that create sonic waves to explore the geology beneath the ocean floor.

Seismic Charge Sonic Mine Sound Effect


The layers of the seafloor are examined with seismic reflection and seismic refraction (also called wide angle seismics). Echosounding is a basic type of seismic reflection. Echosounding is used to measure the depth of the water. High-frequency echosounders (12,000 Hz) are used to measure the depth to the seafloor. A sound pulse is sent from a ship and that sound reflects off the seafloor and returns to the ship. The time the sound takes to travel to the bottom and back is used to calculate the distance to the seafloor (See the section about measuring water depth). Low-frequency echosounders (1,000 to 6,000 Hz) can penetrate a short distance into the seafloor, up to approximately 100 meters, to study the upper sediment layers (see Sub-bottom Profiler).

Seismic refraction gives more information about the layers. Sound pulses that enter the seafloor are both reflected and refracted (or bent) as they pass into different layers. The refracted sound pulse follows a complex path. With seismic refraction, the density of the layers can be determined.

Seismic reflection and refraction can also be done with an instrument on the seafloor called an Ocean Bottom Seismometer (OBS). This instrument is placed on the seafloor and uses sound from artificial and natural sources. A seismic survey may make use of both shipboad measurements and measurements from an array of ocean bottom seismometers.

Steps to identification of a sonic boom:The USGS sees either nothing on our seismic records or a fairly short high-frequency signal that doesn't look like an earthquake.On rare occasions, we see the event on multiple stations, and the time difference between stations matches the speed of sound in air, which is slower than the speed of seismic waves in rock.Felt reports come from a wide area, which...

During seismic surveys, a predominantly low frequency (10 - 300 Hz), high intensity (215-250 dB) sound pulse is emitted every few seconds by the array of guns with the sound pressure depending on the size of the array.

Offshore oil and gas exploration uses deafening seismic surveys that generate the loudest human sounds in the ocean, short of those made by explosives. Seismic testing involves blasting the seafloor with high-powered airguns (a kind of powerful horn) every 10 seconds and measuring the echoes with long tubes to map offshore oil and gas reserves. These blasts disturb, injure and kill marine wildlife around the clock for years on end.

An accurate characterization of underground formations is the key to achieve optimized recovery of geo-energies, particularly in oil and gas reservoirs. Compressional (Vp) and shear (Vs) sonic wave velocities, routinely obtained from seismic surveys and wireline logging, play a first-order role in reservoir evaluation under in-situ conditions. Sonic velocity measurements provide significant insights into formation pore pressure1, rock physical properties, including porosity, pore geometry, pore fluid, and mineralogical content2,3,4, as well as rock stiffness, strength, and brittleness of target strata5, with a wide range of applications from reservoir management and development6 to a variety of geomechanical, geotechnical and geophysical studies7,8. Therefore, in-situ measurements of compressional and shear velocities, frequently using full-waveform recordings, for example, Schlumberger Dipole Sonic Imaging tool (DSI), should be incorporated into the standard practice for reservoir evaluation. However, given the high cost of implementation, borehole conditions, and out-of-date logging tools, acoustic shear velocity measurements are commonly missing spatially across the field or even partially at some intervals along the wellbore. As a consequence, field-scale characterizations primarily require filling in this data shortcoming.

Rock acoustic properties can be directly measured on core specimens in the laboratory. Nevertheless, laboratory measurements are more costly and time-consuming. Furthermore, multiple variables, including pore pressure, temperature, in-situ stresses, pore fluid, saturation degree, and rock mass scale properties, come to influence the sonic wave propagation across the rock2,9,10,11. Replicating in-situ conditions in the laboratory may be challenging and introduce further uncertainties to the measurements. These experimental challenges have motivated researchers to develop shear velocity proxies from wireline logging data. Most notably, collecting velocity data from well logging, seismic, and laboratory measurements, Castagna et al.12 proposed a pioneering predictive model for shear velocity in siliciclastic rocks. They found an approximately linear relationship between shear and compressional velocities.

Castagna and Backus13 adapted the relation as a quadratic function for carbonate rocks. Since then, numerous empirical correlations have been proposed for shear velocity estimation in various rock types and saturated media, mainly in carbonate rocks, broadly encountered as hydrocarbon reservoirs14,15,16,17. Such empirical correlations are advantageous from an implementation point of view because compressional sonic velocity profiles are available in most wells. Eskandari et al.18 incorporated other conventional log suites of gamma-ray (GR), bulk density (RHOB), laterolog deep (LLD), and neutron porosity (NPHI) into a multivariate regression to deal with the potential effects of other environmental, fluid, and rock properties on shear velocity and promote the generalization capability of the models.

The lack of shear velocity measurements has posed significant challenges in conducting geomechanical studies in this area and motivated us to develop a robust predictive model. In the first step, the selection of input variables is of paramount significance. To this end, we seek physically sound relationships between shear velocity (Vs) as the output and other logging data as the inputs. Sonic velocities in carbonate rocks were found to depend primarily on mineralogy and, more importantly, the amount and type of porosity36,37. In formation evaluation, a combination of Vp, GR, RHOB, and NPHI are frequently used for a detailed assessment of mineral contents and rock porosity. We establish two sets of predictive models: first, by using only Vp as the input parameter and then adopting the four well logs as the model variables, from now on referred to as one input and four inputs models, respectively. The reason for developing the former group is to find out how reliable these simple and widely used models are to directly bridge between compressional and shear velocities.

The reliability of the created models can also be inferred from the estimated profiles of Youngs modulus along with the examined formation (Fig. 17). The measured Youngs modulus tracks using modeled shear velocities (ET models and linear regression) and DSI data return almost a perfect match. However, discrepancies arise when comparing vertical distributions of Poissons ratio obtained from the mentioned three models (Fig. 17). The four-variable ET model estimates of shear velocity result in a Poissons ratio profile that is in good agreement with the actual one, i.e., calculated from DSI data. Although the single input ET model satisfactorily captures the general evolution trends of the Poissons ratio across the layer, a perfect quantitative match is missing. This comparison clearly highlights a key and complex dependence of sonic velocity on a set of contributing factors, which a combination of well logging data can only realistically reflect this complexity. This inconsistency may not necessarily pose major uncertainties to our analysis because what matters in candidate selection for hydraulic fracturing is the relative sequence of brittleness and not its absolute value.

We assessed the brittleness profiles along the formation using equation (33) and the predicted elastic moduli. We also employed the well-established k-means clustering technique82 to develop a mechanical rock classification and diagnose rock classes of different brittleness ranges. After a trial-and-error procedure for clustering, we assumed four rock clusters for illustration purposes. One should bear in mind that the number of rock clusters should be determined based on the identified rock types through a detailed geological analysis of recovered cores and thin sections83. Furthermore, for a robust screening of sweet spots, the clustering should also take into account other affecting parameters such as rock porosity, permeability, saturation, and in-situ stresses, which is out of the scope of this study. As expected from elastic moduli predictions, a comparison of brittleness profiles and clusters associated with ET model evaluations and recorded velocities discloses a good agreement (Fig. 19). The lowermost 100 m of the formation and some scattered intervals in its middle and top (light and dark green clusters) are found to have relatively higher brittleness compared to the adjacent zones (purple and red clusters). Therefore, the former groups can be considered as target layers for hydraulic fracturing while the latter potentially act as fracture barriers. However, the regression-based brittleness estimate, inheriting errors from elastic parameter calculations, is not able to follow the overall trends, and the associated fracturing design would be misleading. Briefly, it can be concluded that using linear models to estimate the shear sonic velocity gives rise to certain uncertainties in evaluating the rock Poissons ratio and negatively impacts subsequent geo-mechanical studies. Hence, their application to fill in data gaps should be restricted or treated cautiously. Instead, the employed intelligent approaches provide powerful tools for velocity estimations and should be taken as common practice in the industry. 041b061a72


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